Occlusion Handling

Occlusion handling in computer vision aims to address the challenges posed by objects or parts of objects being hidden from view, a common problem in image and video analysis. Current research focuses on developing robust models, often employing deep learning architectures like Generative Adversarial Networks (GANs) and Transformers, along with innovative techniques such as cumulative occlusion learning and multi-view information integration, to improve object detection, tracking, and pose estimation even in the presence of significant occlusions. This work is crucial for advancing applications such as autonomous driving, human-computer interaction, and robotics, where reliable perception despite occlusions is essential for safe and effective operation.